A Federated Learning Based Privacy Preserving Approach for Detecting Parkinson’s Disease Using Deep Learning

Parkinson’s disease (PD) is a degenerative ailment caused by the loss of nerve cells in the brain region known as the Substantia Nigra, which governs movement. In numerous research papers, traditional machine learning techniques have been utilized for the purpose of PD detection. However, traditional ML algorithms always put a risk on the sensitivity of patients’ data and privacy. This research proposes a novel approach to detecting PD by preserving privacy and security through Federated Learning (FL). FL may train a single algorithm across numerous decentralized local servers as an improved version of the ML approach instead of trading gradient information. The proposed model has been tested and evaluated by using three CNN models (VGG19, VGG16 & InceptionV3) in this research, and within these models, VGG19 has the best accuracy of 97%. The result demonstrates that this model is very accurate for detecting PD by preserving one’s privacy and security using Federated Learning.